Evidence map›Paper›PMID 37438991›Full record

ArticleCNS neuroscience & therapeutics2024

Alzheimer's disease diagnostic accuracy by fluid and neuroimaging ATN framework.

Xi Xiong, Haijun He, Qianqian Ye, Shuangjie Qian, Shuoting Zhou, Feifei Feng, Evandro F Fang, Chenglong Xie

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in CNS neuroscience & therapeutics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
3.8field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.

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  15. The Association Between Brain Metabolic Biomarkers UsingJournal of Alzheimer's disease reports · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 2 countries.

Xi XiongDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Haijun HeDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Qianqian YeDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Shuangjie QianDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Shuoting ZhouDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Feifei FengDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Evandro F FangDepartment of Clinical Molecular Biology, Akershus University Hospital, University of Oslo, Lørenskog, Norway.
Chenglong XieDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.ORCID 0000-0002-1132-0179
Wenzhou Medical University · CNUniversity of Oslo · NO

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
NIA NIH HHS U01 AG024904
6 · The paper itself

Abstract

objectivesThe ATN's different modalities (fluids and neuroimaging) for each of the Aβ (A), tau (T), and neurodegeneration (N) elements are used for the biological diagnosis of Alzheimer's disease (AD). We aim to identify which ATN category achieves the highest potential for diagnosis and predictive accuracy of longitudinal cognitive decline.

methodsBased on the availability of plasma ATN biomarkers (plasma-derived Aβ

resultsOur data analysis indicates that the area under curves (AUCs) of CSF-A, neuroimaging-T, and neuroimaging-N were ranked the top three ATN candidates for accurate diagnosis of AD. Moreover, neuroimaging ATN biomarkers display the best predictive ability for longitudinal cognitive decline among the three categories. To note, neuroimaging-T correlates well with cognitive performances in a negative correlation manner. Meanwhile, participants in the "N" element positive group, especially the CSF-N positive group, experience the fastest cognitive decline compared with other groups defined by ATN biomarkers. In addition, the voxel-wise analysis showed that CSF-A related to tau accumulation and FDG-PET indexes more strongly in subjects with MCI stage. According to our analysis of the data, the best three ATN candidates for a precise diagnosis of AD are CSF-A, neuroimaging-T, and neuroimaging-N.

conclusionsCollectively, our findings suggest that plasma, CSF, and neuroimaging biomarkers differ considerably within the ATN framework; the most accurate target biomarkers for diagnosing AD were the CSF-A, neuroimaging-T, and neuroimaging-N within each ATN modality. Moreover, neuroimaging-T and CSF-N both show excellent ability in the prediction of cognitive decline in two different dimensions.

Indexed as

Alzheimer DiseaseCognitive DysfunctionAmyloid beta-PeptidesBiomarkersFluorodeoxyglucose F18HumansNeuroimagingPositron-Emission Tomographytau ProteinsAmyloid beta-PeptidesBiomarkersFluorodeoxyglucose F18tau ProteinsADNIAlzheimer's diseaseATN biomarkers

Identifiers

PMID37438991
PMCPMC10848089
OpenAlexW4384120131

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.